Spectral estimation methods attempt to account for the interaction of light, sensor and surface in forming a colour image, and to provide algorithms for inverting image formation: recovering spectral quantities from the camera RGB (or other colour space) values.
Solving this inverse problem is very hard, yet understanding the spectra that drive image formation is very important in many applications including photography, computer vision (and visual inspection), forensic imaging, medical applications and also understanding luminance response in low light levels.
The standard image formation equation is set out in equation (1) below:
                              ρ          _                =                  f          ⁡                      (                                                            ∫                  ω                                ⁢                                                                            Q                      _                                        ⁡                                          (                      λ                      )                                                        ⁢                                      E                    ⁡                                          (                      λ                      )                                                        ⁢                                      S                    ⁡                                          (                      λ                      )                                                        ⁢                                      ⅆ                    λ                                                              +                              n                _                                      )                                              (        1        )            
Here, Q(λ) is the spectral sensitivity of the imaging device (for example an eye or camera), E(λ) is the spectral power distribution of the illumination and S(λ) is the % spectral reflectance function. Integration is across the visible spectrum ω (which is often modelled as 400 to 700 nm). Noise is represented by the 3-vector n and f( ) models non-linearities in the sensor and in image encoding. Common non-linearities include gamma (RGBs are raised to the power of 1/2.2 to compensate for the non linearity of display devices), camera curves and tone-curves. Given only ρ, that is, only the 3 RGB camera measurements, it is clearly tremendously difficult to estimate the continuous functions of wavelength (Q(λ), E(λ), or S(λ)).
Because of its complexity and dependence on so many factors, spectral estimation is not typically considered and, instead, expensive pre-calibrated equipment is specified and/or complex expensive measurement systems that operate best in controlled environments may be used.
Clearly, for the average camera user, this is neither practical nor cost effective. Similarly with the advent of smartphones, while applications for their use are advancing significantly (with “apps” for medical practitioners amongst others being very popular), equipment such as the phones' camera is much of an afterthought.